Experiment with Random Difficulty (results based on the confidence scale)
## Nombre de participants à l'expérimentation : 58## Nombre de participants se déclarant comme joueurs : 29## Nombre de femmes se déclarant comme joueuses : 3## Age médian des joueurs : 15Removing Outliers based on BET
(pas nécessaire pour la mesure basée sur l’échelle de confiance)
{r removing.outliers.setup.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #
{r detect.outliers.bet.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS BET STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(miseNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(miseNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(miseNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Bet Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers BET STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.win.sum.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.sheeps.saved.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET SAVED SHEEPS:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.dda.exploit.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET EXPLOIT DDA:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.summary.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #
{r remove.outliers.bet, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTAll <- data.table() # DTAll <- rbind(DTAll,DTL) # DTAll <- rbind(DTAll,DTM) # DTAll <- rbind(DTAll,DTS) #
Removing Outliers based on CONFIDENCE SCALE
## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers CS SAVED SHEEPS: "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers CS EXPLOIT DDA: vuq3c2tk6"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 10"## [1] "Total number of outliers motor task: 0"## [1] "Total number of outliers perceptive task: 5"## [1] "Total number of outliers logical task: 8"Modeling difficulties
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2016.5 2038.2 -1004.3 2008.5 1678
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1935 -0.7469 0.2908 0.7381 2.8784
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.559 0.7476
## Number of obs: 1682, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0580 0.1843 -5.74 9.48e-09 ***
## difficulty 3.0160 0.2115 14.26 < 2e-16 ***
## timeNorm -0.5213 0.1990 -2.62 0.00879 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.540
## timeNorm -0.572 -0.008## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1682 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.05422
## 1st Qu.:-0.44100
## Median :-0.11748
## Mean :-0.00241
## 3rd Qu.: 0.33077
## Max. : 1.65790
## [1] "Intercept: -1.06 9.5e-09 ***"
## [1] "Difficulty: 3.02 3.8e-46 ***"
## [1] "Time: -0.521 0.0088 **"
## [1] "R2 fixed: 0.17"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"## 0% 25% 50% 75% 100%
## -1.6579021 -0.3307656 0.1174780 0.4410031 1.0542161
## 0% 25% 50% 75% 100%
## -1.6579021 -0.3307656 0.1174780 0.4410031 1.0542161
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1131.6 1152.7 -561.8 1123.6 1446
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1980 -0.3704 0.1177 0.3458 6.1390
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7184 0.8476
## Number of obs: 1450, groups: IDjoueur, 50
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1408 0.2668 -11.772 <2e-16 ***
## difficulty 8.0878 0.4208 19.219 <2e-16 ***
## timeNorm -0.4433 0.2833 -1.565 0.118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.631
## timeNorm -0.511 -0.077## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1450
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.666981
## 1st Qu.:-0.446178
## Median : 0.061001
## Mean :-0.001145
## 3rd Qu.: 0.422346
## Max. : 1.471194
## [1] "Intercept: -3.14 5.5e-32 ***"
## [1] "Difficulty: 8.09 2.6e-82 ***"
## [1] "Time: -0.443 0.12 :("
## [1] "R2 fixed: 0.3"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1100"## 0% 25% 50% 75% 100%
## -1.47119372 -0.42234637 -0.06100097 0.44617818 1.66698065
## 0% 25% 50% 75% 100%
## -1.47119372 -0.42234637 -0.06100097 0.44617818 1.66698065
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1444.5 1465.8 -718.2 1436.5 1533
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0357 -0.4980 -0.1017 0.5004 5.0622
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.57 1.253
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9054 0.2628 -7.251 4.14e-13 ***
## difficulty 5.7562 0.3198 18.001 < 2e-16 ***
## timeNorm -1.9355 0.2564 -7.550 4.35e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.497
## timeNorm -0.376 -0.233## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1537 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.8051717
## 1st Qu.:-0.7513212
## Median :-0.2064150
## Mean :-0.0003176
## 3rd Qu.: 0.7228639
## Max. : 3.1492300
## [1] "Intercept: -1.91 4.1e-13 ***"
## [1] "Difficulty: 5.76 1.9e-72 ***"
## [1] "Time: -1.94 4.4e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3393, p-value = 0.1805
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1375478
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.0196, p-value = 0.3079
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1132275
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.12965, p-value = 0.8968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01388433Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.86388, p-value = 0.3877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08757052
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.61918, p-value = 0.5358
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.0679803
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.6523, p-value = 0.5142
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.06919576Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.16967, p-value = 0.8653
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02270513## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1307, p-value = 0.03311
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.304417
##
## [1] "self.eff.on.level.s 0.3 0.033 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.46598, p-value = 0.6412
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06648267Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3157, p-value = 0.1883
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.127906
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.3373, p-value = 0.01943
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2455088
##
## [1] "risk.av.on.level.s 0.25 0.019 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3781, p-value = 0.1682
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1404273Age and level for each task
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.1261, p-value = 0.2601
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1063448## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 1 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8528, p-value = 0.06391
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.189264
##
## [1] "age.on.level.s 0.19 0.064 ."## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1451, p-value = 0.2522
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1130316Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.3774, p-value = 0.01743
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2593202
##
## [1] "sexe.on.level.m -0.26 0.017 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.18718, p-value = 0.8515
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02204982
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.38949, p-value = 0.6969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04451521
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 227, p-value = 0.01687
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8465888 -0.1080105
## sample estimates:
## difference in location
## -0.4966452
##
## [1] "sexe.on.level.m.2 -0.5 0.017 * mean(A): 0.16 mean(B): -0.32"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 281, p-value = 0.8612
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4166427 0.5657028
## sample estimates:
## difference in location
## 0.02816739
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 302, p-value = 0.7064
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7753238 0.5708569
## sample estimates:
## difference in location
## -0.06017729CONFIDENCE SCALE APPROACH
For Bet approach, see the other file.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0790 43 0.00098 ***
## 2: 0.09375 0.1200 55 4.5e-05 ***
## 3: 0.15625 0.1100 57 0.00023 ***
## 4: 0.21875 0.1500 58 1.1e-06 ***
## 5: 0.28125 0.1200 56 9.3e-05 ***
## 6: 0.34375 0.1100 57 2.5e-05 ***
## 7: 0.40625 0.0830 56 0.014 *
## 8: 0.46875 0.0150 57 0.48 :(
## 9: 0.53125 -0.0063 55 0.56 :(
## 10: 0.59375 -0.0600 58 0.0022 **
## 11: 0.65625 -0.0980 58 7.7e-05 ***
## 12: 0.71875 -0.1200 57 3.6e-06 ***
## 13: 0.78125 -0.1700 55 1.3e-07 ***
## 14: 0.84375 -0.2200 52 1.8e-08 ***
## 15: 0.90625 -0.2300 55 4.2e-10 ***
## 16: 0.96875 -0.1800 55 1.3e-09 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 43 0.00098 ***
## 2: 55 4.5e-05 ***
## 3: 57 0.00023 ***
## 4: 58 1.1e-06 ***
## 5: 56 9.3e-05 ***
## 6: 57 2.5e-05 ***
## 7: 56 0.014 *
## 8: 57 0.48 :(
## 9: 55 0.56 :(
## 10: 58 0.0022 **
## 11: 58 7.7e-05 ***
## 12: 57 3.6e-06 ***
## 13: 55 1.3e-07 ***
## 14: 52 1.8e-08 ***
## 15: 55 4.2e-10 ***
## 16: 55 1.3e-09 ***
## [1] 55.2
## [1] 3.62
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.063 27 0.042 *
## 2: 0.09375 0.098 31 0.0065 **
## 3: 0.15625 0.094 41 0.013 *
## 4: 0.21875 0.130 40 0.00028 ***
## 5: 0.28125 0.150 36 0.00077 ***
## 6: 0.34375 0.130 36 0.00016 ***
## 7: 0.40625 0.094 39 0.0018 **
## 8: 0.46875 0.056 36 0.022 *
## 9: 0.53125 0.044 36 0.29 :(
## 10: 0.59375 -0.044 39 0.24 :(
## 11: 0.65625 -0.036 34 0.4 :(
## 12: 0.71875 -0.120 35 0.00028 ***
## 13: 0.78125 -0.100 33 0.0095 **
## 14: 0.84375 -0.200 21 0.0013 **
## 15: 0.90625 -0.240 17 0.0018 **
## 16: 0.96875 -0.130 5 0.31 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 27 0.042 *
## 2: 31 0.0065 **
## 3: 41 0.013 *
## 4: 40 0.00028 ***
## 5: 36 0.00077 ***
## 6: 36 0.00016 ***
## 7: 39 0.0018 **
## 8: 36 0.022 *
## 9: 36 0.29 :(
## 10: 39 0.24 :(
## 11: 34 0.4 :(
## 12: 35 0.00028 ***
## 13: 33 0.0095 **
## 14: 21 0.0013 **
## 15: 17 0.0018 **
## 16: 5 0.31 :(
## [1] 31.6
## [1] 9.73
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0520 30 0.19 :(
## 2: 0.09375 0.1600 33 0.015 *
## 3: 0.15625 0.0940 34 0.021 *
## 4: 0.21875 0.1400 35 0.0032 **
## 5: 0.28125 0.1400 37 0.016 *
## 6: 0.34375 0.0460 36 0.33 :(
## 7: 0.40625 -0.0062 38 0.88 :(
## 8: 0.46875 -0.0440 36 0.5 :(
## 9: 0.53125 -0.0310 36 0.28 :(
## 10: 0.59375 -0.0940 36 0.01 *
## 11: 0.65625 -0.1600 39 0.00011 ***
## 12: 0.71875 -0.1400 38 0.00052 ***
## 13: 0.78125 -0.1600 35 0.00017 ***
## 14: 0.84375 -0.2200 38 6.2e-06 ***
## 15: 0.90625 -0.2300 37 4.2e-07 ***
## 16: 0.96875 -0.1900 34 2.5e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 30 0.19 :(
## 2: 33 0.015 *
## 3: 34 0.021 *
## 4: 35 0.0032 **
## 5: 37 0.016 *
## 6: 36 0.33 :(
## 7: 38 0.88 :(
## 8: 36 0.5 :(
## 9: 36 0.28 :(
## 10: 36 0.01 *
## 11: 39 0.00011 ***
## 12: 38 0.00052 ***
## 13: 35 0.00017 ***
## 14: 38 6.2e-06 ***
## 15: 37 4.2e-07 ***
## 16: 34 2.5e-05 ***
## [1] 35.8
## [1] 2.27
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.120 13 0.09 .
## 2: 0.09375 0.081 25 0.047 *
## 3: 0.15625 0.094 25 0.046 *
## 4: 0.21875 0.056 22 0.09 .
## 5: 0.28125 0.094 23 0.34 :(
## 6: 0.34375 0.160 18 0.00073 ***
## 7: 0.40625 0.130 24 0.089 .
## 8: 0.46875 0.031 25 0.21 :(
## 9: 0.53125 -0.031 22 0.13 :(
## 10: 0.59375 -0.094 26 0.34 :(
## 11: 0.65625 -0.110 27 0.095 .
## 12: 0.71875 -0.120 26 0.018 *
## 13: 0.78125 -0.160 28 0.00079 ***
## 14: 0.84375 -0.240 31 3.4e-05 ***
## 15: 0.90625 -0.210 34 7.7e-06 ***
## 16: 0.96875 -0.220 33 9.7e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 13 0.09 .
## 2: 25 0.047 *
## 3: 25 0.046 *
## 4: 22 0.09 .
## 5: 23 0.34 :(
## 6: 18 0.00073 ***
## 7: 24 0.089 .
## 8: 25 0.21 :(
## 9: 22 0.13 :(
## 10: 26 0.34 :(
## 11: 27 0.095 .
## 12: 26 0.018 *
## 13: 28 0.00079 ***
## 14: 31 3.4e-05 ***
## 15: 34 7.7e-06 ***
## 16: 33 9.7e-07 ***
## [1] 25.1
## [1] 5.24
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.094 9 0.63 :(
## 3: 0.15625 0.094 29 0.43 :(
## 4: 0.21875 0.069 41 0.037 *
## 5: 0.28125 0.094 47 0.018 *
## 6: 0.34375 0.110 50 0.013 *
## 7: 0.40625 0.069 50 0.074 .
## 8: 0.46875 0.040 51 0.036 *
## 9: 0.53125 0.035 54 0.15 :(
## 10: 0.59375 -0.029 53 0.41 :(
## 11: 0.65625 -0.081 54 0.0085 **
## 12: 0.71875 -0.069 54 0.0029 **
## 13: 0.78125 -0.110 45 0.00073 ***
## 14: 0.84375 -0.170 29 0.0045 **
## 15: 0.90625 -0.210 15 0.018 *
## 16: 0.96875 -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.63 :(
## 2: 29 0.43 :(
## 3: 41 0.037 *
## 4: 47 0.018 *
## 5: 50 0.013 *
## 6: 50 0.074 .
## 7: 51 0.036 *
## 8: 54 0.15 :(
## 9: 53 0.41 :(
## 10: 54 0.0085 **
## 11: 54 0.0029 **
## 12: 45 0.00073 ***
## 13: 29 0.0045 **
## 14: 15 0.018 *
## 15: 6 0.056 .
## [1] 39.1
## [1] 17.2## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.0940 9 0.63 :(
## 3: 0.15625 0.0940 26 0.4 :(
## 4: 0.21875 0.0790 27 0.073 .
## 5: 0.28125 0.1200 25 0.017 *
## 6: 0.34375 0.1100 27 0.0014 **
## 7: 0.40625 0.0690 26 0.032 *
## 8: 0.46875 0.0810 25 0.0095 **
## 9: 0.53125 0.0690 25 0.14 :(
## 10: 0.59375 0.0062 24 0.92 :(
## 11: 0.65625 -0.0400 25 0.33 :(
## 12: 0.71875 -0.0880 24 0.023 *
## 13: 0.78125 -0.0810 15 0.037 *
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.63 :(
## 2: 26 0.4 :(
## 3: 27 0.073 .
## 4: 25 0.017 *
## 5: 27 0.0014 **
## 6: 26 0.032 *
## 7: 25 0.0095 **
## 8: 25 0.14 :(
## 9: 24 0.92 :(
## 10: 25 0.33 :(
## 11: 24 0.023 *
## 12: 15 0.037 *
## [1] 23.2
## [1] 5.46## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 0.0670 14 0.29 :(
## 5: 0.28125 0.0690 21 0.31 :(
## 6: 0.34375 0.0460 22 0.67 :(
## 7: 0.40625 0.0190 22 0.92 :(
## 8: 0.46875 -0.0021 22 0.97 :(
## 9: 0.53125 0.0350 22 0.24 :(
## 10: 0.59375 -0.0770 22 0.2 :(
## 11: 0.65625 -0.1200 22 0.019 *
## 12: 0.71875 -0.0440 23 0.17 :(
## 13: 0.78125 -0.0810 22 0.079 .
## 14: 0.84375 -0.1800 21 0.024 *
## 15: 0.90625 -0.1900 7 0.15 :(
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 14 0.29 :(
## 2: 21 0.31 :(
## 3: 22 0.67 :(
## 4: 22 0.92 :(
## 5: 22 0.97 :(
## 6: 22 0.24 :(
## 7: 22 0.2 :(
## 8: 22 0.019 *
## 9: 23 0.17 :(
## 10: 22 0.079 .
## 11: 21 0.024 *
## 12: 7 0.15 :(
## [1] 20
## [1] 4.71## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 1 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 0.190 2 0.5 :(
## 8: 0.46875 NA 4 NA
## 9: 0.53125 -0.031 7 0.19 :(
## 10: 0.59375 -0.094 7 0.33 :(
## 11: 0.65625 -0.160 7 0.33 :(
## 12: 0.71875 -0.085 7 0.15 :(
## 13: 0.78125 -0.180 8 0.028 *
## 14: 0.84375 -0.160 8 0.1 :(
## 15: 0.90625 -0.210 8 0.055 .
## 16: 0.96875 -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 0.5 :(
## 2: 7 0.19 :(
## 3: 7 0.33 :(
## 4: 7 0.33 :(
## 5: 7 0.15 :(
## 6: 8 0.028 *
## 7: 8 0.1 :(
## 8: 8 0.055 .
## 9: 6 0.056 .
## [1] 6.67
## [1] 1.87## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0520 38 0.16 :(
## 2: 0.09375 0.0310 47 0.2 :(
## 3: 0.15625 0.0770 46 0.31 :(
## 4: 0.21875 0.0310 34 0.3 :(
## 5: 0.28125 -0.0063 36 0.87 :(
## 6: 0.34375 -0.0190 29 0.74 :(
## 7: 0.40625 -0.0310 31 0.62 :(
## 8: 0.46875 -0.1400 31 0.019 *
## 9: 0.53125 -0.1600 27 0.0065 **
## 10: 0.59375 -0.1900 34 0.001 **
## 11: 0.65625 -0.1600 35 0.0012 **
## 12: 0.71875 -0.2200 34 0.00012 ***
## 13: 0.78125 -0.2300 32 4.5e-05 ***
## 14: 0.84375 -0.2400 40 1.3e-05 ***
## 15: 0.90625 -0.2000 48 5.5e-08 ***
## 16: 0.96875 -0.0950 50 6.8e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 38 0.16 :(
## 2: 47 0.2 :(
## 3: 46 0.31 :(
## 4: 34 0.3 :(
## 5: 36 0.87 :(
## 6: 29 0.74 :(
## 7: 31 0.62 :(
## 8: 31 0.019 *
## 9: 27 0.0065 **
## 10: 34 0.001 **
## 11: 35 0.0012 **
## 12: 34 0.00012 ***
## 13: 32 4.5e-05 ***
## 14: 40 1.3e-05 ***
## 15: 48 5.5e-08 ***
## 16: 50 6.8e-07 ***
## [1] 37
## [1] 7.18
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.1600 4 0.25 :(
## 2: 0.09375 0.0940 3 0.75 :(
## 3: 0.15625 0.1600 4 0.58 :(
## 4: 0.21875 0.0310 3 1 :(
## 5: 0.28125 0.1800 4 0.38 :(
## 6: 0.34375 -0.0440 2 1 :(
## 7: 0.40625 -0.1600 2 1 :(
## 8: 0.46875 -0.0560 3 1 :(
## 9: 0.53125 0.1900 2 1 :(
## 10: 0.59375 -0.1700 4 0.38 :(
## 11: 0.65625 NA 1 NA
## 12: 0.71875 NA 3 NA
## 13: 0.78125 0.0021 3 1 :(
## 14: 0.84375 -0.1800 4 0.38 :(
## 15: 0.90625 -0.1200 3 0.25 :(
## 16: 0.96875 -0.0500 4 0.62 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 4 0.25 :(
## 2: 3 0.75 :(
## 3: 4 0.58 :(
## 4: 3 1 :(
## 5: 4 0.38 :(
## 6: 2 1 :(
## 7: 2 1 :(
## 8: 3 1 :(
## 9: 2 1 :(
## 10: 4 0.38 :(
## 11: 3 1 :(
## 12: 4 0.38 :(
## 13: 3 0.25 :(
## 14: 4 0.62 :(
## [1] 3.21
## [1] 0.802## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 21 0.49 :(
## 2: 0.09375 -0.0440 21 0.28 :(
## 3: 0.15625 -0.0560 19 0.2 :(
## 4: 0.21875 0.0310 13 1 :(
## 5: 0.28125 -0.0062 14 0.95 :(
## 6: 0.34375 -0.1400 14 0.032 *
## 7: 0.40625 -0.1100 13 0.14 :(
## 8: 0.46875 -0.2200 14 0.016 *
## 9: 0.53125 -0.3300 13 0.016 *
## 10: 0.59375 -0.3400 14 0.0019 **
## 11: 0.65625 -0.2300 17 0.0059 **
## 12: 0.71875 -0.3200 13 0.005 **
## 13: 0.78125 -0.2300 14 0.0031 **
## 14: 0.84375 -0.3400 15 0.0023 **
## 15: 0.90625 -0.2000 20 0.00031 ***
## 16: 0.96875 -0.0680 21 0.0099 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 21 0.49 :(
## 2: 21 0.28 :(
## 3: 19 0.2 :(
## 4: 13 1 :(
## 5: 14 0.95 :(
## 6: 14 0.032 *
## 7: 13 0.14 :(
## 8: 14 0.016 *
## 9: 13 0.016 *
## 10: 14 0.0019 **
## 11: 17 0.0059 **
## 12: 13 0.005 **
## 13: 14 0.0031 **
## 14: 15 0.0023 **
## 15: 20 0.00031 ***
## 16: 21 0.0099 **
## [1] 16
## [1] 3.25
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.120 13 0.09 .
## 2: 0.09375 0.110 23 0.024 *
## 3: 0.15625 0.094 23 0.053 .
## 4: 0.21875 0.056 18 0.22 :(
## 5: 0.28125 -0.031 18 0.54 :(
## 6: 0.34375 0.090 13 0.024 *
## 7: 0.40625 0.094 16 0.36 :(
## 8: 0.46875 -0.094 14 0.53 :(
## 9: 0.53125 -0.081 12 0.089 .
## 10: 0.59375 -0.094 16 0.24 :(
## 11: 0.65625 -0.160 17 0.085 .
## 12: 0.71875 -0.170 18 0.048 *
## 13: 0.78125 -0.250 15 0.0037 **
## 14: 0.84375 -0.190 21 0.0047 **
## 15: 0.90625 -0.190 25 0.00013 ***
## 16: 0.96875 -0.130 25 6.1e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 13 0.09 .
## 2: 23 0.024 *
## 3: 23 0.053 .
## 4: 18 0.22 :(
## 5: 18 0.54 :(
## 6: 13 0.024 *
## 7: 16 0.36 :(
## 8: 14 0.53 :(
## 9: 12 0.089 .
## 10: 16 0.24 :(
## 11: 17 0.085 .
## 12: 18 0.048 *
## 13: 15 0.0037 **
## 14: 21 0.0047 **
## 15: 25 0.00013 ***
## 16: 25 6.1e-05 ***
## [1] 17.9
## [1] 4.3
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.094 36 0.0044 **
## 2: 0.09375 0.160 41 3.1e-05 ***
## 3: 0.15625 0.170 42 8.4e-05 ***
## 4: 0.21875 0.260 44 3.2e-06 ***
## 5: 0.28125 0.220 36 0.00012 ***
## 6: 0.34375 0.160 40 5.4e-05 ***
## 7: 0.40625 0.094 44 0.0061 **
## 8: 0.46875 0.031 41 0.038 *
## 9: 0.53125 -0.031 38 0.5 :(
## 10: 0.59375 -0.044 42 0.41 :(
## 11: 0.65625 -0.056 40 0.46 :(
## 12: 0.71875 -0.069 39 0.0097 **
## 13: 0.78125 -0.150 44 0.00022 ***
## 14: 0.84375 -0.230 43 2.1e-07 ***
## 15: 0.90625 -0.260 42 4.7e-07 ***
## 16: 0.96875 -0.350 27 6.1e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 36 0.0044 **
## 2: 41 3.1e-05 ***
## 3: 42 8.4e-05 ***
## 4: 44 3.2e-06 ***
## 5: 36 0.00012 ***
## 6: 40 5.4e-05 ***
## 7: 44 0.0061 **
## 8: 41 0.038 *
## 9: 38 0.5 :(
## 10: 42 0.41 :(
## 11: 40 0.46 :(
## 12: 39 0.0097 **
## 13: 44 0.00022 ***
## 14: 43 2.1e-07 ***
## 15: 42 4.7e-07 ***
## 16: 27 6.1e-06 ***
## [1] 39.9
## [1] 4.3
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.050 26 0.071 .
## 2: 0.09375 0.110 26 0.007 **
## 3: 0.15625 0.120 25 0.015 *
## 4: 0.21875 0.210 24 0.0013 **
## 5: 0.28125 0.150 18 0.074 .
## 6: 0.34375 0.160 21 0.046 *
## 7: 0.40625 0.120 22 0.04 *
## 8: 0.46875 0.031 20 0.44 :(
## 9: 0.53125 -0.031 19 0.5 :(
## 10: 0.59375 -0.094 22 0.12 :(
## 11: 0.65625 -0.073 17 0.42 :(
## 12: 0.71875 -0.100 19 0.034 *
## 13: 0.78125 -0.130 22 0.013 *
## 14: 0.84375 -0.240 19 0.00097 ***
## 15: 0.90625 -0.310 16 0.0029 **
## 16: 0.96875 NA 1 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 26 0.071 .
## 2: 26 0.007 **
## 3: 25 0.015 *
## 4: 24 0.0013 **
## 5: 18 0.074 .
## 6: 21 0.046 *
## 7: 22 0.04 *
## 8: 20 0.44 :(
## 9: 19 0.5 :(
## 10: 22 0.12 :(
## 11: 17 0.42 :(
## 12: 19 0.034 *
## 13: 22 0.013 *
## 14: 19 0.00097 ***
## 15: 16 0.0029 **
## [1] 21.1
## [1] 3.17## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.2200 10 0.032 *
## 2: 0.09375 0.4100 13 0.0019 **
## 3: 0.15625 0.3400 14 0.0015 **
## 4: 0.21875 0.3200 14 0.0019 **
## 5: 0.28125 0.2200 12 0.002 **
## 6: 0.34375 0.1600 11 0.004 **
## 7: 0.40625 0.0940 13 0.35 :(
## 8: 0.46875 0.0310 12 0.027 *
## 9: 0.53125 -0.0310 11 0.61 :(
## 10: 0.59375 0.0063 11 0.5 :(
## 11: 0.65625 -0.1300 13 0.037 *
## 12: 0.71875 -0.0190 12 0.31 :(
## 13: 0.78125 -0.2400 13 0.0061 **
## 14: 0.84375 -0.2200 14 0.0016 **
## 15: 0.90625 -0.2800 14 0.002 **
## 16: 0.96875 -0.3500 14 0.0012 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 10 0.032 *
## 2: 13 0.0019 **
## 3: 14 0.0015 **
## 4: 14 0.0019 **
## 5: 12 0.002 **
## 6: 11 0.004 **
## 7: 13 0.35 :(
## 8: 12 0.027 *
## 9: 11 0.61 :(
## 10: 11 0.5 :(
## 11: 13 0.037 *
## 12: 12 0.31 :(
## 13: 13 0.0061 **
## 14: 14 0.0016 **
## 15: 14 0.002 **
## 16: 14 0.0012 **
## [1] 12.6
## [1] 1.31
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 2 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 0.1600 6 0.29 :(
## 5: 0.28125 0.3700 6 0.058 .
## 6: 0.34375 0.2700 8 0.029 *
## 7: 0.40625 0.1900 9 0.12 :(
## 8: 0.46875 0.1300 9 0.41 :(
## 9: 0.53125 0.0190 8 0.94 :(
## 10: 0.59375 0.0063 9 1 :(
## 11: 0.65625 0.0690 10 0.22 :(
## 12: 0.71875 -0.0690 8 0.44 :(
## 13: 0.78125 -0.0310 9 0.55 :(
## 14: 0.84375 -0.2500 10 0.014 *
## 15: 0.90625 -0.1900 12 0.011 *
## 16: 0.96875 -0.3500 12 0.0025 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 6 0.29 :(
## 2: 6 0.058 .
## 3: 8 0.029 *
## 4: 9 0.12 :(
## 5: 9 0.41 :(
## 6: 8 0.94 :(
## 7: 9 1 :(
## 8: 10 0.22 :(
## 9: 8 0.44 :(
## 10: 9 0.55 :(
## 11: 10 0.014 *
## 12: 12 0.011 *
## 13: 12 0.0025 **
## [1] 8.92
## [1] 1.85## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71195 -0.16836 0.00376 0.17619 0.63833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.182297 0.019774 9.219 <2e-16 ***
## timeNorm 0.005893 0.020913 0.282 0.778
## obj.diff -0.375586 0.025858 -14.525 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05568596)
##
## Null deviance: 105.649 on 1681 degrees of freedom
## Residual deviance: 93.497 on 1679 degrees of freedom
## AIC: -79.355
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81832 -0.18077 -0.03144 0.21140 0.81844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04638 0.01869 2.482 0.0132 *
## timeNorm 0.04996 0.02477 2.017 0.0439 *
## obj.diff -0.27350 0.01929 -14.177 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06919415)
##
## Null deviance: 114.36 on 1449 degrees of freedom
## Residual deviance: 100.12 on 1447 degrees of freedom
## AIC: 247.2
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73430 -0.20594 -0.01949 0.19850 0.71398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21759 0.02001 10.88 <2e-16 ***
## timeNorm 0.05914 0.02495 2.37 0.0179 *
## obj.diff -0.53045 0.02119 -25.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06995631)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 107.31 on 1534 degrees of freedom
## AIC: 278.57
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5422414 0.6014885 -0.0544661582 116 0.038 *
## 2: 4.5 0.5367816 0.5712048 -0.0274664169 174 0.17 :(
## 3: 7.5 0.5155172 0.5413406 -0.0217582878 174 0.28 :(
## 4: 10.5 0.5413793 0.5404361 0.0102982443 174 0.62 :(
## 5: 13.5 0.5155172 0.5181081 -0.0005152110 174 0.97 :(
## 6: 16.5 0.5310345 0.5333167 -0.0007660154 174 0.97 :(
## 7: 19.5 0.5063218 0.5344527 -0.0290711237 174 0.12 :(
## 8: 22.5 0.4873563 0.4934513 -0.0053069701 174 0.8 :(
## 9: 25.5 0.4890805 0.4822968 0.0047959969 174 0.8 :(
## 10: 28.5 0.4741379 0.4548030 0.0173421720 174 0.4 :(
## time error.diff shapes
## 1: 1.5 -0.0544661582 24
## 2: 4.5 -0.0274664169 16
## 3: 7.5 -0.0217582878 16
## 4: 10.5 0.0102982443 16
## 5: 13.5 -0.0005152110 16
## 6: 16.5 -0.0007660154 16
## 7: 19.5 -0.0290711237 16
## 8: 22.5 -0.0053069701 16
## 9: 25.5 0.0047959969 16
## 10: 28.5 0.0173421720 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4700000 0.5997864 -0.13975600 100 3.6e-05 ***
## 2: 4.5 0.5140000 0.6275717 -0.09648920 150 3.6e-07 ***
## 3: 7.5 0.4680000 0.5393273 -0.07378323 150 0.00087 ***
## 4: 10.5 0.5200000 0.5921954 -0.06766340 150 0.00055 ***
## 5: 13.5 0.4733333 0.5792956 -0.09407597 150 4.9e-07 ***
## 6: 16.5 0.4246667 0.5271146 -0.10651377 150 4.4e-06 ***
## 7: 19.5 0.4820000 0.5466728 -0.05111224 150 0.0024 **
## 8: 22.5 0.5026667 0.5836131 -0.06894162 150 0.00099 ***
## 9: 25.5 0.5526667 0.6047896 -0.03482444 150 0.041 *
## 10: 28.5 0.5046667 0.5734270 -0.06386275 150 0.0018 **
## time error.diff shapes
## 1: 1.5 -0.13975600 24
## 2: 4.5 -0.09648920 24
## 3: 7.5 -0.07378323 24
## 4: 10.5 -0.06766340 24
## 5: 13.5 -0.09407597 24
## 6: 16.5 -0.10651377 24
## 7: 19.5 -0.05111224 24
## 8: 22.5 -0.06894162 24
## 9: 25.5 -0.03482444 24
## 10: 28.5 -0.06386275 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4415094 0.6007697 -1.658770e-01 106 3.8e-06 ***
## 2: 4.5 0.5119497 0.6324837 -1.343840e-01 159 4.2e-06 ***
## 3: 7.5 0.5100629 0.5479813 -4.895619e-02 159 0.069 .
## 4: 10.5 0.5220126 0.5177334 2.196993e-03 159 0.93 :(
## 5: 13.5 0.5169811 0.5303606 -2.035258e-02 159 0.43 :(
## 6: 16.5 0.5100629 0.5026471 2.226322e-05 159 1 :(
## 7: 19.5 0.4584906 0.4514766 -3.401739e-03 159 0.87 :(
## 8: 22.5 0.4226415 0.4287566 -1.335901e-02 159 0.6 :(
## 9: 25.5 0.4584906 0.3964332 6.936761e-02 159 0.013 *
## 10: 28.5 0.4446541 0.3652666 6.326623e-02 159 0.012 *
## time error.diff shapes
## 1: 1.5 -1.658770e-01 24
## 2: 4.5 -1.343840e-01 24
## 3: 7.5 -4.895619e-02 16
## 4: 10.5 2.196993e-03 16
## 5: 13.5 -2.035258e-02 16
## 6: 16.5 2.226322e-05 16
## 7: 19.5 -3.401739e-03 16
## 8: 22.5 -1.335901e-02 16
## 9: 25.5 6.936761e-02 24
## 10: 28.5 6.326623e-02 24
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.80095 -0.22147 -0.03275 0.22938 0.68419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16218 0.02381 6.811 1.48e-11 ***
## timeNorm 0.09045 0.02599 3.480 0.000518 ***
## obj.diff -0.44649 0.02419 -18.454 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06803532)
##
## Null deviance: 113.557 on 1304 degrees of freedom
## Residual deviance: 88.582 on 1302 degrees of freedom
## AIC: 200.94
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74643 -0.19670 -0.01898 0.22254 0.79171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13170 0.01938 6.795 1.49e-11 ***
## timeNorm 0.04458 0.02347 1.899 0.0577 .
## obj.diff -0.38377 0.02214 -17.336 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07247264)
##
## Null deviance: 146.61 on 1710 degrees of freedom
## Residual deviance: 123.78 on 1708 degrees of freedom
## AIC: 370.01
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70383 -0.18819 -0.00456 0.18741 0.70555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14635 0.01756 8.334 <2e-16 ***
## timeNorm 0.03140 0.02183 1.438 0.151
## obj.diff -0.32907 0.02375 -13.854 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0577491)
##
## Null deviance: 107.759 on 1652 degrees of freedom
## Residual deviance: 95.286 on 1650 degrees of freedom
## AIC: -17.765
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5433333 0.7425351 -0.21536339 90 2.4e-09 ***
## 2: 4.5 0.5977778 0.7592898 -0.18795253 135 7.6e-09 ***
## 3: 7.5 0.5903704 0.6992252 -0.11767343 135 2.1e-05 ***
## 4: 10.5 0.6051852 0.6890762 -0.08511592 135 0.0018 **
## 5: 13.5 0.5962963 0.7197816 -0.14192728 135 2.4e-06 ***
## 6: 16.5 0.5614815 0.6614001 -0.11184615 135 7.5e-05 ***
## 7: 19.5 0.6096296 0.6600605 -0.05003101 135 0.02 *
## 8: 22.5 0.6066667 0.6925415 -0.09028078 135 0.0031 **
## 9: 25.5 0.6000000 0.6630481 -0.05902781 135 0.038 *
## 10: 28.5 0.6125926 0.6599147 -0.04280764 135 0.077 .
## time error.diff shapes
## 1: 1.5 -0.21536339 24
## 2: 4.5 -0.18795253 24
## 3: 7.5 -0.11767343 24
## 4: 10.5 -0.08511592 24
## 5: 13.5 -0.14192728 24
## 6: 16.5 -0.11184615 24
## 7: 19.5 -0.05003101 24
## 8: 22.5 -0.09028078 24
## 9: 25.5 -0.05902781 24
## 10: 28.5 -0.04280764 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4822034 0.5975971 -0.111436388 118 0.00092 ***
## 2: 4.5 0.5129944 0.6352660 -0.114767070 177 2.2e-07 ***
## 3: 7.5 0.4847458 0.5177747 -0.036915251 177 0.097 .
## 4: 10.5 0.5305085 0.5631287 -0.033213315 177 0.12 :(
## 5: 13.5 0.4915254 0.5309774 -0.039453248 177 0.033 *
## 6: 16.5 0.4887006 0.5405487 -0.054493501 177 0.012 *
## 7: 19.5 0.4474576 0.5349227 -0.089474298 177 2.3e-05 ***
## 8: 22.5 0.4536723 0.5141464 -0.065474535 177 0.0041 **
## 9: 25.5 0.5316384 0.5267600 -0.001073905 177 0.96 :(
## 10: 28.5 0.4689266 0.4775807 -0.016818264 177 0.45 :(
## time error.diff shapes
## 1: 1.5 -0.111436388 24
## 2: 4.5 -0.114767070 24
## 3: 7.5 -0.036915251 16
## 4: 10.5 -0.033213315 16
## 5: 13.5 -0.039453248 24
## 6: 16.5 -0.054493501 24
## 7: 19.5 -0.089474298 24
## 8: 22.5 -0.065474535 24
## 9: 25.5 -0.001073905 16
## 10: 28.5 -0.016818264 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4464912 0.4920024 -0.039373153 114 0.15 :(
## 2: 4.5 0.4701754 0.4628310 0.007698767 171 0.74 :(
## 3: 7.5 0.4415205 0.4454963 -0.003335300 171 0.87 :(
## 4: 10.5 0.4654971 0.4238930 0.050240849 171 0.014 *
## 5: 13.5 0.4409357 0.4106373 0.034723539 171 0.1 :(
## 6: 16.5 0.4380117 0.3907548 0.045897839 171 0.025 *
## 7: 19.5 0.4198830 0.3683684 0.047552721 171 0.02 *
## 8: 22.5 0.3812865 0.3337882 0.044708722 171 0.04 *
## 9: 25.5 0.3847953 0.3211868 0.061789353 171 0.0019 **
## 10: 28.5 0.3695906 0.2900987 0.065986425 171 0.0017 **
## time error.diff shapes
## 1: 1.5 -0.039373153 16
## 2: 4.5 0.007698767 16
## 3: 7.5 -0.003335300 16
## 4: 10.5 0.050240849 24
## 5: 13.5 0.034723539 16
## 6: 16.5 0.045897839 24
## 7: 19.5 0.047552721 24
## 8: 22.5 0.044708722 24
## 9: 25.5 0.061789353 24
## 10: 28.5 0.065986425 24
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65081 -0.16600 -0.07689 0.21864 0.38438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.29746 0.07745 3.841 0.000159 ***
## timeNorm 0.03979 0.04731 0.841 0.401279
## obj.diff -0.59239 0.08830 -6.709 1.52e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.03968561)
##
## Null deviance: 10.995 on 231 degrees of freedom
## Residual deviance: 9.088 on 229 degrees of freedom
## AIC: -85.242
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.6250000 0.8541813 -0.23116534 16 0.0013 **
## 2: 4.5 0.6375000 0.7984136 -0.16995810 24 0.0053 **
## 3: 7.5 0.6208333 0.7533950 -0.13245689 24 0.014 *
## 4: 10.5 0.6375000 0.7827081 -0.15599626 24 0.0079 **
## 5: 13.5 0.6250000 0.8239746 -0.20561865 24 4.4e-05 ***
## 6: 16.5 0.6375000 0.7813561 -0.15210779 24 0.027 *
## 7: 19.5 0.6541667 0.7252246 -0.07066985 24 0.14 :(
## 8: 22.5 0.6458333 0.7650575 -0.12329390 24 0.049 *
## 9: 25.5 0.6583333 0.7912822 -0.13403150 24 0.0072 **
## 10: 28.5 0.6166667 0.7394780 -0.11089775 24 0.042 *
## time error.diff shapes
## 1: 1.5 -0.23116534 24
## 2: 4.5 -0.16995810 24
## 3: 7.5 -0.13245689 24
## 4: 10.5 -0.15599626 24
## 5: 13.5 -0.20561865 24
## 6: 16.5 -0.15210779 24
## 7: 19.5 -0.07066985 16
## 8: 22.5 -0.12329390 24
## 9: 25.5 -0.13403150 24
## 10: 28.5 -0.11089775 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7128 -0.1799 0.0080 0.1979 0.6542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.160601 0.040886 3.928 9.46e-05 ***
## timeNorm 0.003705 0.038216 0.097 0.923
## obj.diff -0.347965 0.054087 -6.433 2.39e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07289063)
##
## Null deviance: 51.554 on 666 degrees of freedom
## Residual deviance: 48.399 on 664 degrees of freedom
## AIC: 151.12
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5413043 0.6313063 -0.080414494 46 0.073 .
## 2: 4.5 0.5652174 0.6292224 -0.057371089 69 0.099 .
## 3: 7.5 0.5420290 0.5592216 -0.011452588 69 0.74 :(
## 4: 10.5 0.5463768 0.5820863 -0.022036607 69 0.57 :(
## 5: 13.5 0.5550725 0.5449914 0.012093320 69 0.72 :(
## 6: 16.5 0.5478261 0.5622564 -0.019457251 69 0.62 :(
## 7: 19.5 0.4942029 0.5766338 -0.086681165 69 0.0077 **
## 8: 22.5 0.4681159 0.5121072 -0.050443461 69 0.17 :(
## 9: 25.5 0.5014493 0.4988278 -0.003887111 69 0.93 :(
## 10: 28.5 0.5014493 0.4985043 -0.010272074 69 0.7 :(
## time error.diff shapes
## 1: 1.5 -0.080414494 16
## 2: 4.5 -0.057371089 16
## 3: 7.5 -0.011452588 16
## 4: 10.5 -0.022036607 16
## 5: 13.5 0.012093320 16
## 6: 16.5 -0.019457251 16
## 7: 19.5 -0.086681165 24
## 8: 22.5 -0.050443461 16
## 9: 25.5 -0.003887111 16
## 10: 28.5 -0.010272074 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61019 -0.15879 0.00778 0.17071 0.53696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13601 0.02536 5.362 1.08e-07 ***
## timeNorm 0.01638 0.02758 0.594 0.553
## obj.diff -0.23883 0.03902 -6.121 1.47e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0443567)
##
## Null deviance: 36.420 on 782 degrees of freedom
## Residual deviance: 34.598 on 780 degrees of freedom
## AIC: -212.38
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5185185 0.5012163 0.020987089 54 0.54 :(
## 2: 4.5 0.4827160 0.4544613 0.033464592 81 0.19 :(
## 3: 7.5 0.4617284 0.4632778 0.001764609 81 0.95 :(
## 4: 10.5 0.5086420 0.4331719 0.087371264 81 0.0012 **
## 5: 13.5 0.4493827 0.4045805 0.051873659 81 0.053 .
## 6: 16.5 0.4851852 0.4351713 0.052762168 81 0.042 *
## 7: 19.5 0.4728395 0.4419957 0.028567591 81 0.25 :(
## 8: 22.5 0.4567901 0.3970833 0.064184236 81 0.019 *
## 9: 25.5 0.4283951 0.3766636 0.052786293 81 0.026 *
## 10: 28.5 0.4086420 0.3332279 0.073327560 81 0.0036 **
## time error.diff shapes
## 1: 1.5 0.020987089 16
## 2: 4.5 0.033464592 16
## 3: 7.5 0.001764609 16
## 4: 10.5 0.087371264 24
## 5: 13.5 0.051873659 16
## 6: 16.5 0.052762168 24
## 7: 19.5 0.028567591 16
## 8: 22.5 0.064184236 24
## 9: 25.5 0.052786293 24
## 10: 28.5 0.073327560 24
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.82477 -0.23539 0.03098 0.21098 0.74594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11642 0.02843 4.094 4.71e-05 ***
## timeNorm 0.06183 0.03534 1.750 0.0806 .
## obj.diff -0.34886 0.02868 -12.164 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07034251)
##
## Null deviance: 61.516 on 724 degrees of freedom
## Residual deviance: 50.787 on 722 degrees of freedom
## AIC: 138.03
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5200000 0.6474550 -0.15492519 50 0.0013 **
## 2: 4.5 0.6000000 0.7033552 -0.09866131 75 0.0013 **
## 3: 7.5 0.5520000 0.6266810 -0.07859938 75 0.02 *
## 4: 10.5 0.5733333 0.6435238 -0.05813045 75 0.045 *
## 5: 13.5 0.5720000 0.6726818 -0.09478286 75 0.0062 **
## 6: 16.5 0.4733333 0.5640040 -0.10527469 75 0.0042 **
## 7: 19.5 0.5666667 0.5977412 -0.03027251 75 0.34 :(
## 8: 22.5 0.6200000 0.6652746 -0.03274302 75 0.28 :(
## 9: 25.5 0.5666667 0.6198083 -0.04700838 75 0.18 :(
## 10: 28.5 0.5866667 0.6385647 -0.04211572 75 0.18 :(
## time error.diff shapes
## 1: 1.5 -0.15492519 24
## 2: 4.5 -0.09866131 24
## 3: 7.5 -0.07859938 24
## 4: 10.5 -0.05813045 24
## 5: 13.5 -0.09478286 24
## 6: 16.5 -0.10527469 24
## 7: 19.5 -0.03027251 16
## 8: 22.5 -0.03274302 16
## 9: 25.5 -0.04700838 16
## 10: 28.5 -0.04211572 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7551 -0.1193 -0.0176 0.2119 0.7742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03446 0.02642 -1.304 0.193
## timeNorm 0.05445 0.03682 1.479 0.140
## obj.diff -0.20652 0.02819 -7.326 7.59e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06417815)
##
## Null deviance: 42.444 on 608 degrees of freedom
## Residual deviance: 38.892 on 606 degrees of freedom
## AIC: 60.889
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4214286 0.5551116 -0.12535215 42 0.02 *
## 2: 4.5 0.4047619 0.5559055 -0.11747754 63 9.6e-06 ***
## 3: 7.5 0.3730159 0.4568975 -0.07838708 63 0.0061 **
## 4: 10.5 0.4571429 0.5749516 -0.10829165 63 3.4e-05 ***
## 5: 13.5 0.3587302 0.4801693 -0.08929886 63 1.4e-05 ***
## 6: 16.5 0.3650794 0.4960746 -0.11459640 63 2e-04 ***
## 7: 19.5 0.3698413 0.4966815 -0.08499877 63 0.00032 ***
## 8: 22.5 0.3825397 0.5250806 -0.12114824 63 4.1e-05 ***
## 9: 25.5 0.5714286 0.6211134 -0.02451455 63 0.15 :(
## 10: 28.5 0.4380952 0.5322281 -0.08349773 63 0.0027 **
## time error.diff shapes
## 1: 1.5 -0.12535215 24
## 2: 4.5 -0.11747754 24
## 3: 7.5 -0.07838708 24
## 4: 10.5 -0.10829165 24
## 5: 13.5 -0.08929886 24
## 6: 16.5 -0.11459640 24
## 7: 19.5 -0.08499877 24
## 8: 22.5 -0.12114824 24
## 9: 25.5 -0.02451455 16
## 10: 28.5 -0.08349773 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.52336 -0.18845 -0.08358 0.22260 0.67092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19558 0.06225 3.142 0.00214 **
## timeNorm -0.08478 0.08549 -0.992 0.32348
## obj.diff -0.33681 0.06986 -4.821 4.48e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06541813)
##
## Null deviance: 8.9339 on 115 degrees of freedom
## Residual deviance: 7.3922 on 113 degrees of freedom
## AIC: 17.827
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4125000 0.5363999 -0.117749663 8 0.25 :(
## 2: 4.5 0.5500000 0.5301724 0.018012073 12 0.79 :(
## 3: 7.5 0.4416667 0.4261225 0.002921262 12 1 :(
## 4: 10.5 0.5166667 0.3619221 0.159012057 12 0.092 .
## 5: 13.5 0.4583333 0.5160446 -0.097753146 12 0.3 :(
## 6: 16.5 0.4333333 0.4595159 -0.040985432 12 0.52 :(
## 7: 19.5 0.5416667 0.4899492 0.033162305 12 0.57 :(
## 8: 22.5 0.4000000 0.3805246 0.020316455 12 0.85 :(
## 9: 25.5 0.3666667 0.4252229 -0.036094200 12 0.68 :(
## 10: 28.5 0.3416667 0.3826097 -0.069493080 12 0.3 :(
## time error.diff shapes
## 1: 1.5 -0.117749663 16
## 2: 4.5 0.018012073 16
## 3: 7.5 0.002921262 16
## 4: 10.5 0.159012057 16
## 5: 13.5 -0.097753146 16
## 6: 16.5 -0.040985432 16
## 7: 19.5 0.033162305 16
## 8: 22.5 0.020316455 16
## 9: 25.5 -0.036094200 16
## 10: 28.5 -0.069493080 16## Warning: Removed 4 rows containing missing values (geom_errorbar).
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71234 -0.14997 -0.08784 0.27401 0.49346
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42192 0.06134 6.879 2.85e-11 ***
## timeNorm 0.10326 0.05356 1.928 0.0547 .
## obj.diff -0.79993 0.06044 -13.235 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07237563)
##
## Null deviance: 39.895 on 347 degrees of freedom
## Residual deviance: 24.970 on 345 degrees of freedom
## AIC: 78.76
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5375000 0.8661879 -0.33559332 24 1.2e-06 ***
## 2: 4.5 0.5666667 0.8497378 -0.29748250 36 8.1e-06 ***
## 3: 7.5 0.6500000 0.8142455 -0.19127733 36 0.009 **
## 4: 10.5 0.6500000 0.7215556 -0.07446844 36 0.22 :(
## 5: 13.5 0.6277778 0.7484441 -0.16881290 36 0.046 *
## 6: 16.5 0.6944444 0.7843380 -0.10382299 36 0.1 :(
## 7: 19.5 0.6694444 0.7464496 -0.07302888 36 0.088 .
## 8: 22.5 0.5527778 0.7010035 -0.15331912 36 0.017 *
## 9: 25.5 0.6305556 0.6676417 -0.01149567 36 0.87 :(
## 10: 28.5 0.6638889 0.6513517 0.00766190 36 0.87 :(
## time error.diff shapes
## 1: 1.5 -0.33559332 24
## 2: 4.5 -0.29748250 24
## 3: 7.5 -0.19127733 24
## 4: 10.5 -0.07446844 16
## 5: 13.5 -0.16881290 24
## 6: 16.5 -0.10382299 16
## 7: 19.5 -0.07302888 16
## 8: 22.5 -0.15331912 24
## 9: 25.5 -0.01149567 16
## 10: 28.5 0.00766190 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64515 -0.11289 -0.01712 0.07668 0.55464
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43304 0.03477 12.456 <2e-16 ***
## timeNorm -0.02149 0.04104 -0.524 0.601
## obj.diff -0.78069 0.03655 -21.359 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05324245)
##
## Null deviance: 48.688 on 434 degrees of freedom
## Residual deviance: 23.001 on 432 degrees of freedom
## AIC: -36.345
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4766667 0.6053895 -0.116541637 30 0.064 .
## 2: 4.5 0.5844444 0.7556376 -0.176715092 45 0.00062 ***
## 3: 7.5 0.5533333 0.5394510 -0.005172903 45 0.95 :(
## 4: 10.5 0.6088889 0.5175084 0.088259333 45 0.17 :(
## 5: 13.5 0.5800000 0.5806209 -0.005113984 45 0.88 :(
## 6: 16.5 0.5711111 0.5695271 -0.010758358 45 0.79 :(
## 7: 19.5 0.4844444 0.5245031 -0.042659346 45 0.36 :(
## 8: 22.5 0.5311111 0.5019653 0.025448995 45 0.68 :(
## 9: 25.5 0.5222222 0.4374948 0.079369402 45 0.11 :(
## 10: 28.5 0.4622222 0.3689916 0.100988341 45 0.069 .
## time error.diff shapes
## 1: 1.5 -0.116541637 16
## 2: 4.5 -0.176715092 24
## 3: 7.5 -0.005172903 16
## 4: 10.5 0.088259333 16
## 5: 13.5 -0.005113984 16
## 6: 16.5 -0.010758358 16
## 7: 19.5 -0.042659346 16
## 8: 22.5 0.025448995 16
## 9: 25.5 0.079369402 16
## 10: 28.5 0.100988341 16## Warning: Removed 1 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65879 -0.19764 -0.04055 0.21062 0.72428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13031 0.02751 4.737 2.59e-06 ***
## timeNorm 0.06201 0.03610 1.718 0.0863 .
## obj.diff -0.38121 0.03503 -10.881 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06928019)
##
## Null deviance: 62.177 on 753 degrees of freedom
## Residual deviance: 52.029 on 751 degrees of freedom
## AIC: 131.88
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3769231 0.4756038 -0.100098761 52 0.041 *
## 2: 4.5 0.4448718 0.4611623 -0.028101324 78 0.43 :(
## 3: 7.5 0.4205128 0.4300114 -0.017591275 78 0.61 :(
## 4: 10.5 0.4128205 0.4237913 -0.009060319 78 0.8 :(
## 5: 13.5 0.4294872 0.4007105 0.033286086 78 0.46 :(
## 6: 16.5 0.3897436 0.3340513 0.051497071 78 0.13 :(
## 7: 19.5 0.3461538 0.2732045 0.067072182 78 0.075 .
## 8: 22.5 0.3000000 0.2608684 0.012950063 78 0.6 :(
## 9: 25.5 0.3423077 0.2475707 0.092260597 78 0.012 *
## 10: 28.5 0.3333333 0.2310782 0.075326505 78 0.051 .
## time error.diff shapes
## 1: 1.5 -0.100098761 24
## 2: 4.5 -0.028101324 16
## 3: 7.5 -0.017591275 16
## 4: 10.5 -0.009060319 16
## 5: 13.5 0.033286086 16
## 6: 16.5 0.051497071 16
## 7: 19.5 0.067072182 16
## 8: 22.5 0.012950063 16
## 9: 25.5 0.092260597 24
## 10: 28.5 0.075326505 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77757 -0.18933 0.01113 0.18652 0.79661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05597 0.01134 4.938 8.68e-07 ***
## est.confidence.norm -0.14072 0.02000 -7.035 2.90e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06108697)
##
## Null deviance: 105.65 on 1681 degrees of freedom
## Residual deviance: 102.63 on 1680 degrees of freedom
## AIC: 75.35
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.90030 -0.16875 0.02763 0.12274 0.97690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.07186 0.01539 -4.668 3.32e-06 ***
## est.confidence.norm -0.02501 0.02636 -0.949 0.343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07892825)
##
## Null deviance: 114.36 on 1449 degrees of freedom
## Residual deviance: 114.29 on 1448 degrees of freedom
## AIC: 437.06
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.95702 -0.21545 -0.02456 0.22257 0.95697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05462 0.01698 3.217 0.00132 **
## est.confidence.norm -0.12831 0.02840 -4.518 6.72e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1006412)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 154.48 on 1535 degrees of freedom
## AIC: 836.57
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1058.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5037 -0.6521 0.0056 0.6065 3.5080
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01081 0.1040
## Residual 0.07097 0.2664
## Number of obs: 4669, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.759e-03 1.618e-02 9.200e+01 -0.294 0.76927
## est.confidence.norm -5.519e-02 1.529e-02 4.597e+03 -3.609 0.00031 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.475
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -642.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1060 -0.6742 -0.0493 0.7179 3.1837
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02811 0.1677
## Residual 0.03562 0.1887
## Number of obs: 1682, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01775 0.02581 91.60000 -0.687 0.494
## est.confidence.norm 0.01288 0.02640 1544.90000 0.488 0.626
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.491
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 306
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2244 -0.6635 0.0627 0.5607 3.8020
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01250 0.1118
## Residual 0.06741 0.2596
## Number of obs: 1450, groups: IDjoueur, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.12001 0.02487 136.00000 -4.826 3.69e-06 ***
## est.confidence.norm 0.06893 0.03500 776.20000 1.969 0.0493 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.722
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 685.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9615 -0.6605 -0.0590 0.6527 3.2759
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01927 0.1388
## Residual 0.08483 0.2913
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04730 0.02869 130.10000 -1.649 0.1016
## est.confidence.norm 0.06558 0.03824 912.70000 1.715 0.0867 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.701## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1058.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5037 -0.6521 0.0056 0.6065 3.5080
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01081 0.1040
## Residual 0.07097 0.2664
## Number of obs: 4669, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.759e-03 1.618e-02 9.200e+01 -0.294 0.76927
## est.confidence.norm -5.519e-02 1.529e-02 4.597e+03 -3.609 0.00031 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.475
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -642.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1060 -0.6742 -0.0493 0.7179 3.1837
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02811 0.1677
## Residual 0.03562 0.1887
## Number of obs: 1682, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01775 0.02581 91.60000 -0.687 0.494
## est.confidence.norm 0.01288 0.02640 1544.90000 0.488 0.626
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.491
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 306
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2244 -0.6635 0.0627 0.5607 3.8020
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01250 0.1118
## Residual 0.06741 0.2596
## Number of obs: 1450, groups: IDjoueur, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.12001 0.02487 136.00000 -4.826 3.69e-06 ***
## est.confidence.norm 0.06893 0.03500 776.20000 1.969 0.0493 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.722
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 685.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9615 -0.6605 -0.0590 0.6527 3.2759
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01927 0.1388
## Residual 0.08483 0.2913
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04730 0.02869 130.10000 -1.649 0.1016
## est.confidence.norm 0.06558 0.03824 912.70000 1.715 0.0867 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.701Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.64049, p-value = 0.5219
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03918709Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.5015, p-value = 0.01237
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1512796
##
## [1] "pbg.on.error -0.15 0.012 *"In game level and subjective difficulty error
## [1] "niveau.group.on.error.s 0.073 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1741, p-value = 0.0297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1161331
##
## [1] "niveau.group.on.error 0.12 0.03 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.241, p-value = 0.2146
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1119177
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1962, p-value = 0.2316
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1167347
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.4421, p-value = 0.1493
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1364296Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.8899, p-value = 0.003854
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1881991
##
## [1] "sexe.on.error 0.19 0.0039 **"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2674, p-value = 0.205
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1382439
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9341, p-value = 0.0531
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2278481
##
## [1] "sexe.on.error.s 0.23 0.053 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8362, p-value = 0.06633
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2098574
##
## [1] "sexe.on.error.l 0.21 0.066 ."
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 3474, p-value = 0.03634
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.001924049 0.080768238
## sample estimates:
## difference in location
## 0.04138762
##
## [1] "sexe.on.error.2 0.041 0.036 * mean(A): -0.051 mean(B): -0.0017"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 429, p-value = 0.3397
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.02974738 0.10327763
## sample estimates:
## difference in location
## 0.03654071
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 335, p-value = 0.1958
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.0182461 0.1202237
## sample estimates:
## difference in location
## 0.04431821
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 397, p-value = 0.1744
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.01803927 0.12221408
## sample estimates:
## difference in location
## 0.04695392Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.64038, p-value = 0.5219
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03711019
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.46154, p-value = 0.6444
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04486755
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.2406, p-value = 0.8099
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02527296
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.37011, p-value = 0.7113
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03771476Self efficacy and subjective difficulty error
## Warning: Removed 80 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.7267, p-value = 0.006397
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2156646
##
## [1] "self.eff.on.error -0.22 0.0064 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.3008, p-value = 0.1933
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1740727## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.3761, p-value = 0.1688
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1966026## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.9749, p-value = 0.04828
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2817599
##
## [1] "self.eff.on.error -0.28 0.048 *"